Modeling Complex Temporal Composition of Actionlets for Activity Prediction

2012 ◽  
pp. 286-299 ◽  
Author(s):  
Kang Li ◽  
Jie Hu ◽  
Yun Fu
Keyword(s):  
Activity Prediction

Efficacy and Activity Prediction by Molecular Topology of New Drugs Against the Tetranychus urticae Plague

2013 ◽  
Vol 16 (6) ◽  
pp. 473-483 ◽  
Author(s):  
Salvador Mérida ◽  
Santos Fustero ◽  
Vincent M. Villar ◽  
María Gálvez ◽  
Raquel Román ◽  
...  
Keyword(s):  
Tetranychus Urticae ◽  
New Drugs ◽  
Molecular Topology ◽  
Activity Prediction

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

Personalized Context-aware Collaborative Online Activity Prediction

2019 ◽  
Vol 3 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Yali Fan ◽  
Zhen Tu ◽  
Yong Li ◽  
Xiang Chen ◽  
Hui Gao ◽  
...  
Keyword(s):  
Context Aware ◽  
Activity Prediction ◽  
Online Activity

In silico consensus activity prediction, rational synthesis, and evaluation of antiglycation and antiplatelet activities of 3,6-disubstituted 1,2,4,5-tetrazines

2020 ◽  
Vol 69 (4) ◽  
pp. 768-773
Author(s):  
R. I. Ishmetova ◽  
D. A. Babkov ◽  
A. F. Kucheryavenko ◽  
V. A. Babkova ◽  
V. S. Sirotenko ◽  
...  
Keyword(s):  
In Silico ◽  
Activity Prediction

scholarly journals DESIGN AND ANTICANCER ACTIVITY PREDICTION OF DIHYROPYRIMIDINONE BASED NOVEL INHIBITORS OF P53-MDM2 INTERACTION

2017 ◽  
Vol 10 (16) ◽  
pp. 110
Author(s):  
Surendra Kumar Nayak ◽  
Gopal Lal Khatik ◽  
Rakesh Narang ◽  
Harish Kumar Chopra
Keyword(s):  
Anticancer Activity ◽  
Binding Affinity ◽  
Anticancer Agents ◽  
P53 Protein ◽  
Docking Study ◽  
Molecular Docking Study ◽  
Activity Prediction ◽  
Cycle Regulation ◽  
Mdm2 Inhibitor ◽  
Better Than

  Objective: P53 protein is well known for its role in cell cycle regulation and induction of apoptosis. This protein is degraded by MDM2 mediated proteolysis. Inhibition of interaction between p53 and MDM2 has been recognized as a most potential and selective target for development of novel anticancer agents. Recently, several molecules entered in the clinical trial study for the treatment of various types of cancers are based on inhibition of interaction between p53-MDM2. Therefore, in this study, a novel dihydropyridine based molecules were designed as p53-MDM2 inhibitor, and their anticancer activity (including reference) was determined in comparison with most active anticancer agent and inactive anticancer agents in National Cancer Institute database using “Cancer IN” server.Methods: In this work, a novel dihydropyrimidinone based lead (L11) on the basis of molecular docking study, predicted IC50, anticancer activity, and toxicity profile were designed. Lead L11 was obtained after sequential isosteric replacement of functional groups for optimization in compound L0.Results: The docking scores of L3-L11 found to be in range of 21-25 close to docking score 25 of SAR405838 and better than nutlin-3a. MDM2 binding affinity values (37-78 Kcal/mol) of all ligands were also found to better than that of nutlin-3a (37 Kcal/mol). Surprisingly, MDM2 binding affinity of L11 (78 Kcal/mol) found to be equal to that of SAR405838 and 2-fold greater than nutlin-3a.Conclusion: These data indicating that L11 as a potential lead from dihydropyrimidinones for inhibition of p53-MDM2 interaction.

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